Many video compression file coding standards exist, including the Motion Picture Experts Group (MPEG) MPEG1 standard for video cassette recorders (VCD) and MPEG2 standard for digital video disks (DVD), the International Telecommunications Union (ITU) H.261, H.263, and H.263+ standards, and the H.264/MPEG4 AVC standard jointly developed by the MPEG and ITU organizations. Another coding standard is the Joint Photographic Motion Group (JPEG) motion JPEG standard. Many of these standards code the source image by first decomposing the source image into small image blocks, e.g., 8×8 or 4×4 blocks, apply a Discrete Cosine Transform (DCT) to the image blocks, and quantify transfer coefficients. This process often involves quantifying noise and transferring error bits that occur during the DCT application to the image blocks.
During the quantification of DCT transfer coefficients, the process abandons many near-zero high frequency coefficients. Quantification, thus, causes precision loss termed quantifying effect. The quantifying effect causes noise that is proportional to the quantifying step: the larger the step, the larger the noise.
And the transfer or transmission of error bits associated with quantifying noise results in a large energy difference between error ridden and adjacent non error ridden DCT blocks. The adjacent non error ridden DCT blocks will have larger phase steps that adversely impact image quality. We collectively refer to the abandonment of near zero coefficients and the transfer of error bits associated with quantifying noise as DCT coefficient error or block effect.
Image compression typically includes decoding a, zooming b, trimming c, and hindering d, as is shown in FIG. 1. Reducing the block effect often occurs during or after decoding a. Many have described reducing the block effect including Liu and Bovik in Efficient DCT-domain Blind Measurement and Reduction Of Blocking Antifacts, G. A. Triantafyllidis and others in Blockness Detection in Compressed Data, Wesley F. Miaw in Implementation of Real-Time Software-only Image Smoothing Filter for a Block-transfbrm Video Codec, and K. Ramkishor in A Simple and Efficient Deblocking Algorithm for Low Bit-Rate Video Coding. 
Reducing the block effect using these and other current methods that occur during or after decoding may be improved. This is because the decoded image in many systems, including televisions and computer monitors, perform zooming, trimming, and hindering after decoding and thus, after reducing, that may adversely impact the effectiveness of reducing the block effect. And all current methods for reducing the block effect adopt low pass filter to filter, ignoring the different features of the block effect in textural and smoothing regions. In smoothing regions of decoded video images, DCT coefficient errors introduce a phase step along the borders of adjacent blocks. High frequency information in the smoothing region is insufficient to make the phase step small, but the human eye is sensitive enough to detect changes in the smoothing region. In the textural regions of decoded video images where plentiful high frequency information exists, the quantifying error is larger. Even though the error is large, the human eye is insensitive to the high frequency noise and thus, insensitive to the block effect in textural regions. Reducing the block effect may be improved because current methods do not independently adjust filtering according to the human eye's sensitivity to smoothing regions and insensitivity to textural regions to thereby improve image quality.